Seismic data reconstruction method based on morphological component analysis Online publication date: Thu, 07-Apr-2022
by Jianhong Yao; Jicheng Liu; Ya Gu; Yongxin Chou
International Journal of Modelling, Identification and Control (IJMIC), Vol. 37, No. 3/4, 2021
Abstract: The real seismic data is usually under-sampled in space domain because of the physical or economic limitations. So the incomplete seismic data need to be reconstructed before the subsequent processing. A method based on morphological component analysis (MCA) is discussed in the paper, which uses the curvelet dictionary and local discrete cosine transform (LDCT) dictionary to reconstruct the smooth components and the singular components respectively. Block coordinate relaxation (BCR) algorithm is adopted to complete the sparse optimisation. The validity of the proposed method was tested by numerical experiments on synthetic and real data demonstrate. As the method based on curvelet combining with POCS is widely used in practice, we compare its reconstructed results with the MCA-based method. The numerical results validate that the proposed method has higher reconstruction performance.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook